Tag: customer journey

On Tuesday I went to the BDA Conference on Big Data Analytics. Conferences like these are always interesting to see at a high-level how analytics and its uses are evolving. This conference was no different and some of the trends that came through the various sessions suggest where future opportunities will be to leverage analytics:

A big challenge, and opportunity, is integrating data from multiple sources to get a more complete picture of your customers. Until recently, analyzing data in your product was the primary way to understand users (and play patterns in games) but now there is valuable data available from multiple sources. Data from social media (what people are saying about you and your product, sentiment, etc), data from beacons and other sensors, data from user acquisition, etc. When you integrate this data, you get a more complete understanding of your users and their motivations.

Data is connecting people and things, expanding the universe of data. There is now extensive data on how people interact with their surroundings and this will grow.

Using data is moving from the province of data scientists and analysts to everyone in the organization. This trend is driven by easier to use and manipulate tools, not by increased training. Designers and product managers and marketers are not becoming data experts but the tools now allow easy visualization, point and click charts, swipe and pinch access.

Top companies are now using the various data sources to understand holistically the customer journey and then driving activities to increase the value from the customer during their journey. The critical change is that you are using different data sources to pick up the user at different points (think of a race with cameras along the course and how the telecast switches between cameras).

People are now using, and expecting, data on a real time basis. Increasingly everyone in the organization has real time access to data and can drive actions based on this information. No longer are people waiting for the charts on yesterday’s activity.

Key takeaways

The universe of data is exploding, with multiple data sources and good analytics now blends this data to provide a complete picture of the customer.

Data is no longer being controlled by a few people in BI (business intelligence), user-friendly tools are allowing everyone in the organization to access and control data easily to enhance their decision-making

Data allows companies to see the entire customer journey, with different data sources filling in different parts of the journey.

There was a great article in the Harvard Business Review, “The Truth About Customer Experience,” that shows more importantly than focusing on providing the customer with good discrete interactions you should focus on the entire journey. Interestingly, even if you have great metrics at each touch point (e.g., people are satisfied with onboarding, customer services call are resolved positively), overall customer satisfaction may be negative because of the holistic customer journey.

The article uses the example of new customer onboarding for a pay TV provider to show how the journey can be negative even when each touchpoint gets positive feedback. As the article describes, “Take new-customer onboarding, a journey that typically spans about three months and involves six or so phone calls, a home visit from a technician, and numerous web and mail exchanges. Each interaction with this provider had a high likelihood of going well. But in key customer segments, average satisfaction fell almost 40% over the course of the journey. It wasn’t the touchpoints that needed to be improved—it was the onboarding process as a whole. Most service encounters were positive in a narrow sense—employees resolved the issues at hand—but the underlying problems were avoidable, the fundamental causes went unaddressed, and the cumulative effect on the customer was decidedly negative.”

The root of the problem is that many customer focused functions (sales, CS, community management) are siloed in different organizations that have individual and insular cultures. These groups shape how the company interacts with consumers but although they may aim to optimize their contributions they lose focus of the customers desires.

A key to predicting and effectively using customer lifetime value (LTV) is to take a long-term view of your data and not just rely on the first month or even first few days. Many marketers will draw conclusions about a new product launch, a new feature or a unique customer cohort based on the initial data they generate. While you cannot wait months or years to make crucial business decisions, understand that these predictions are less reliable and thus making decisions based on this data is problematic.

The challenges

While intuitively more data is always better, there are challenges involved in looking back over a long period. First among these challenges is customer attribution. If you are determining the value of a specific growth channel, do you credit the lifetime spend of a user to the channel you used to acquire them initially or do you attribute the revenue to a channel (Facebook feed, email, A2U notification, etc.,) that brought the user back after a long period of inactivity.

The second issue is the sheer quantity of data. If you have millions of customers or players and years of data, it becomes quite a challenge to process all of that data. You may have multiple interactions with that user every day, literally for years. Think of how you interact with Amazon and consider they track all the products you look at, how often you visit, what you purchase, what you purchase instead, etc. You need the software, data warehousing and systems so that you can actually analyze this data quickly. Continue reading “Lifetime Value Part 22: The need to take a long-term view”

Get my book on LTV

Understanding the Predictable delves into the world of Customer Lifetime Value (LTV), a metric that shows how much each customer is worth to your business. By understanding this metric, you can predict how changes to your product will impact the value of each customer. You will also learn how to apply this simple yet powerful method of predictive analytics to optimize your marketing and user acquisition.

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Lloyd Melnick

This is Lloyd Melnick’s personal blog. I am EVP Casino at VGW, where I lead the Chumba Casino team. I am a serial builder of businesses (senior leadership on three exits worth over $700 million), successful in big (Disney, Stars Group, Zynga) and small companies (Merscom, Spooky Cool Labs) with over 20 years experience in the gaming and casino space.